The best behavioral AI models will finally belong to health plans, not to sellers

Behavioral health crises rarely manifest overnight. For health plans, warning panels are hidden at sight – such as dispersed data points on disconnected systems, locked in obsolete or missing records.

With regard to physical health, health plans have analytical skills to predict complications, hospitalizations, costs and other results with remarkable precision, but behavioral health analysis did not historically have the same level of rigor. Complaint data, DSE files and the results declared by patients can all provide valuable information on the image of a member’s behavioral health and the quality of the care he receives, but they are almost always retrospective. In addition, these ideas are often not integrated, painting an incomplete image – or sometimes completely inaccurate – of the care and results of a patient.

For health plans managing high -risk populations, this reactive position can be expensive. The possibility of knowing whether a member receives the right level of care for the appropriate duration and that the care provided is of high quality, is invaluable; It allows health plans to intervene before a member is disengaging from care or undergoing a crisis.

For example, an AI model could analyze existing complaint data to identify high -risk members receiving insufficient care. The algorithm could detect that a member with a history of multiple psychiatric hospitalizations only do monthly therapy sessions, when high -risk similar profiles generally require weekly sessions to prevent a crisis. With this information, the plan can then proactively allow additional care before the member knew another costly hospitalization.

Obtaining these types of information is entirely possible with the progress of AI models and automatic learning for behavioral health, but they raise a central question for health plans: how should these models be developed and managed? Should they be purchased from internal partners or integrated?

Option A: buy behavioral health data models

Some will inevitably decide on the latter, and since local analysis models generally do not have the priority for behavioral health, this approach has a lot of merit. However, many predefined models on the market promise instant risk forecasts and standard conviviality that is not realistic when you take into account the complexity and diversity of population health on a national scale. Although a carefully packaged model is attractive, there are latent costs, namely limited visibility in the way a model has been formed, on the data on which it is based and under what conditions it will be reliable. Without this understanding, health plans essentially bet their care strategies on a black box.

Behavioral health is notoriously dynamic. Even at the level of the population, social conditions and economic realities can change quickly. A predictive model that is precise today can underform the underperforming tomorrow if it is not maintained and recycled in real time. Health plans that choose to buy risks are locked in static models that could very quickly become outdated. When this happens, they do not lack capacity to adapt the tool – they do not have the capacity to explain its shortcomings. When the model is mistaken (that each model will be, at some point), these health plans must be ready to explain Why to its stakeholders. If they cannot articulate what is wrong, or how they intend to repair it, confidence instantly crosses.

There is also an additional element to the test of the future here. Regulatory executives of AI and ML in health care almost certainly descends the pike. If it is impossible to know exactly what form this surveillance will take, it is sure to assume that it will require greater transparency, explanability and responsibility.

Option B: Building behavioral health data models

The decision to build comes with your own obstacles to entry, but it is also the safest way to keep the property of your models.

The property is crucial, but it is not only a question of goods – it is a question of ensuring that the design of the model is aligned with the organizational objective. When health plans control their predictive models, they have ultimate control over the priorities that are cooked there. They can audit biases and recycle models on new evidence. They can ensure that it is settled not only for predictive precision, but for the exploitable results which reflect the needs of a population, supplier networks and the commitment strategies of the members.

It’s more than just day care. It is a commitment to people that data represent. Investment in this commitment can make the difference between a tool that identifies the risks on paper and that really changes the trajectory of someone’s life.

Many health plans are not ready to do it entirely internally today. These plans should look for partners with expertise that include the importance of the property of the model and are able to ensure that the internal teams are ready to maintain it over time.

The best of construction and purchase: advisory analysis

Predictive models are strategic assets that must be fed. Occupational health plans now do to build, own and refine them will permanently determine not only their ability to manage risks and costs, but also their ability to adapt to the future of health care. By maintaining their predictive models, health plans will be ready to respect this future. More importantly, they will be positioned to ensure that these powerful models serve the best interests of their members.

This is the approach that Neuroflow adopts with its BHIQ analysis solution. Rather than offering cut-cookie algorithms, Neuroflow works directly with health plans to personalize the automatic learning models formed to identify the hidden risks for behavioral health and adapt algorithms for their populations, supplier networks and data environments.

This advisory approach guarantees the sensitivity, accuracy and security of the model data, while providing health plans a complete transparency in the operation of algorithms. Health plans are working hand in hand with scientists from Neuroflow data to shape the models of the model, ensure clinical relevance and continuously monitor performance thanks to real -time feedback loops. The result is a predictive intelligence which is not only explainable, but instantly exploitable.

Learn more On how BHIQ can help your plan create predictive models in which you can trust.